1. Data: Import, clean and pre-process the data
  1. EDA and visualisation: Create a detailed performance report using univariate, bi-variate and multivariate EDA techniques. Find out all possible hidden
  1. Classifier: Design and train a best fit SVM classier using all the data attributes.
  1. Dimensional reduction: perform dimensional reduction on the data.
  1. Classifier: Design and train a best fit SVM classier using dimensionally reduced attributes.
  1. Conclusion: Showcase key pointer on how dimensional reduction helped in this case.